Tensorflow¶
Keras Multi-layer Perceptron
Tensorflow Premade Estimators
Tensorflow Image and Text Classification
Image Classification with TensorFlow
This article is based on the TensorFlow Image Classification article where we demonstrate image classification using TensorFlow. The dataset that we use here is a filtered version of Dogs vs Cats dataset from Kaggle.
Dogs vs. Cats Dataset
Downloading Data¶
In [1]:
import os
import tensorflow as tf
def Get_Data(_URL, Remove = True):
# The dataset URL
File = _URL.split('/')[-1]
Full_Name = os.path.join(os.getcwd(), File)
# Download the dataset file from the URL
path_to_zip = tf.keras.utils.get_file(fname =Full_Name, origin=_URL, extract=True, cache_dir = os.getcwd())
PATH = os.path.join(os.path.dirname(path_to_zip), 'datasets', File.split('.')[0])
# Deleting the zip file
if Remove:
os.remove(File)
return PATH
#-----------------------------------------------------------------
_URL = 'https://storage.googleapis.com/mledu-datasets/cats_and_dogs_filtered.zip'
PATH = Get_Data(_URL)
Dataset Directory Structure¶
In [2]:
from HD_DirTools import Path_Tree
Path_Tree(PATH)
In [3]:
from HD_DirTools import Data_Info
DataFrame_Info, DataDirs = Data_Info(PATH)
display(DataFrame_Info.set_index(['Set' , 'Subset']).T)
Preprocessing¶
In [4]:
batch_size = 128
epochs = 15
Img_Height = 150
Img_Width = 150
Train Data Image DataGenerator¶
In [5]:
image_gen_train = tf.keras.preprocessing.image.ImageDataGenerator(
# Rescaling the tensors from values between 0 and 255 to values between 0 and 1
rescale=1./255,
# Applying 45 degrees of rotation randomly
rotation_range=45,
# Range for random horizontal shifts.
width_shift_range=.15,
# Range for random vertical shifts.
height_shift_range=.15,
# applying random horizontal flip augmentation
horizontal_flip=True,
# Applying a zoom augmentation to the dataset to zoom images up to 50%
zoom_range=0.5
)
print('Train Data:')
# flow_from_directory method load images from the disk
train_data_gen = image_gen_train.flow_from_directory(batch_size=batch_size,
directory=DataDirs['train'],
shuffle=True,
target_size=(Img_Height, Img_Width),
class_mode='binary')
In [6]:
import matplotlib.pyplot as plt
def plotImages(images_arr, s = 3.4, Title = False):
fig, axes = plt.subplots(1, len(images_arr), figsize=(s* len(images_arr),s))
axes = axes.flatten()
for img, ax in zip( images_arr, axes):
ax.imshow(img)
ax.axis('off')
_ = fig.tight_layout()
_ = fig.subplots_adjust(wspace= 5e-3)
if Title:
_ = fig.suptitle(Title, y = 1.05, weight = 'bold', fontsize = 18)
sample_images, _ = next(train_data_gen)
plotImages(sample_images[:4], Title = 'Four Random Pictures from the Train Sample')
augmented_images = [train_data_gen[0][0][0] for i in range(5)]
plotImages(augmented_images, Title = 'Original and Augmented Pictures', s = 3)
Validation Data Image DataGenerator¶
In [7]:
validation_image_generator = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1./255)
print('Validation Data:')
val_data_gen = validation_image_generator.flow_from_directory(batch_size = batch_size,
directory = DataDirs['validation'],
target_size = (Img_Height, Img_Width),
class_mode = 'binary')
sample_images, _ = next(val_data_gen)
plotImages(sample_images[:4], Title = 'Four Random Pictures from the Train Sample')
Modeling¶
We use Keras Sequential model for creating a model.
In [8]:
model = tf.keras.models.Sequential(name = 'CNN')
model.add(tf.keras.layers.Conv2D(16, 3, padding='same', activation='relu', input_shape=(Img_Height, Img_Width ,3)))
model.add(tf.keras.layers.MaxPooling2D())
# regularization
model.add(tf.keras.layers.Dropout(0.2))
model.add(tf.keras.layers.Conv2D(32, 3, padding='same', activation='relu'))
model.add(tf.keras.layers.MaxPooling2D())
model.add(tf.keras.layers.Conv2D(64, 3, padding='same', activation='relu'))
model.add(tf.keras.layers.MaxPooling2D())
# regularization
model.add(tf.keras.layers.Dropout(0.2))
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(512, activation='relu'))
model.add(tf.keras.layers.Dense(1))
model.summary()
tf.keras.utils.plot_model(model, show_shapes=True, show_layer_names=False, expand_nested = True, rankdir = 'TB')
Out[8]:
Compiling and fitting the model
In [9]:
model.compile(optimizer = 'adam', loss = tf.keras.losses.BinaryCrossentropy(from_logits=True), metrics = ['accuracy'])
history = model.fit(train_data_gen,
steps_per_epoch = DataFrame_Info.loc[DataFrame_Info['Set'] == 'Train','Size'].sum() // batch_size,
epochs = epochs,
validation_data = val_data_gen,
validation_steps = DataFrame_Info.loc[DataFrame_Info['Set'] == 'Validation','Size'].sum() // batch_size)
# clear_output()
In [10]:
def Search_List(Key, List): return [s for s in List if Key in s]
Metrics_Names = {'loss':'Loss', 'accuracy':'Accuracy', 'mae':'MAE', 'mse':'MSE', 'recall': 'Recall'}
import pandas as pd
import numpy as np
from HD_DeepLearning import history2Table
Validation_Table = Search_List('val_',history.history.keys())
Train_Table = list(set( history.history.keys()) - set(Validation_Table))
Validation_Table = pd.DataFrame(np.array([history.history[x] for x in Validation_Table]).T, columns = Validation_Table)
Train_Table = pd.DataFrame(np.array([history.history[x] for x in Train_Table]).T, columns = Train_Table)
Validation_Table.columns = [x.replace('val_','') for x in Validation_Table.columns]
Train_Table = history2Table(Train_Table, Metrics_Names)
Validation_Table = history2Table(Validation_Table, Metrics_Names)
# Train Set Score
score = model.evaluate(train_data_gen, batch_size = batch_size, verbose = 0)
score = pd.DataFrame(score, index = model.metrics_names).T
score.index = ['Train Set Score']
# Validation Set Score
Temp = model.evaluate(val_data_gen, batch_size = batch_size, verbose = 0)
Temp = pd.DataFrame(Temp, index = model.metrics_names).T
Temp.index = ['Validation Set Score']
score = pd.concat([score, Temp])
score.rename(columns= Metrics_Names, inplace = True)
score = score.reindex(sorted(score.columns), axis=1)
display(score.style.format(precision=4))
In [11]:
from HD_DeepLearning import Plot_history
PD = dict(row_heights = [0.4, 0.6], lw = 1.5, font_size=12, height = 700, yLim = 1,
th_line_color = 'Navy', th_fill_color='darkslategray', table_columnwidth = [0.4, 0.4, 0.4, 0.4],
tc_line_color = 'Navy', tc_fill_color = None, title_x = 0.46, title_y = 0.94, tb_cell_heigh = 20,
Number_Format = '%.4e')
Plot_history(Train_Table, PD, Title = 'Train Set', Table_Rows = 10, Colors = ['DarkGreen', 'Red'])
Plot_history(Validation_Table, PD, Title = 'Validation Set', Table_Rows = 10, Colors = [ 'DarkGreen', 'Red'])
Here, we only went through a few iterations; however, we need to train the model for more iterations to get acceptable results.
In [12]:
# del downloaded images
import shutil
shutil.rmtree('datasets/cats_and_dogs_filtered')